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The revolution in computer hardware, especially in graphics processing unitsand tensor processing units, has enabled significant advances in computergraphics and artificial intelligence algorithms. In addition to their manybeneficial applications in daily life and business,computer-generated/manipulated images and videos can be used for maliciouspurposes that violate security systems, privacy, and social trust. The deepfakephenomenon and its variations enable a normal user to use his or her personalcomputer to easily create fake videos of anybody from a short real onlinevideo. Several countermeasures have been introduced to deal with attacks usingsuch videos. However, most of them are targeted at certain domains and areineffective when applied to other domains or new attacks. In this paper, weintroduce a capsule network that can detect various kinds of attacks, frompresentation attacks using printed images and replayed videos to attacks usingfake videos created using deep learning. It uses many fewer parameters thantraditional convolutional neural networks with similar performance. Moreover,we explain, for the first time ever in the literature, the theory behind theapplication of capsule networks to the forensics problem through detailedanalysis and visualization.

Use of a Capsule Network to Detect Fake Images and Videos